Given a new discrete data vector z, determine which class it belongs to.
Given a new discrete data vector z, determine which class it belongs to. Classify returns 1 meaning 'z' belongs to the positive class, while -1 means it belongs to the negative class.
the vector to classify
Given a new continuous data vector z, determine which class it belongs to.
Given a new continuous data vector z, determine which class it belongs to. Classify returns 1 meaning 'z' belongs to the positive class, while -1 means it belongs to the negative class.
the vector to classify
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
Convert svm to a string showing (w, b).
Convert svm to a string showing (w, b).
Train uses SMO (Sequential Minimum Optimization) algorithm to solves the optimization problem for the weight vector 'w' and the threshold 'b' for the model '(w dot z) - b'.
Train uses SMO (Sequential Minimum Optimization) algorithm to solves the optimization problem for the weight vector 'w' and the threshold 'b' for the model '(w dot z) - b'.
Update weights 'w' and error cache 'fCache'.
Update weights 'w' and error cache 'fCache'.
the index for the first Lagrange multipliers (alpha)
the index for the second Lagrange multipliers (alpha)
the first target value
the second target value
The
SupportVectorMachine
class is a translation of Pseudo-Code from a modified SMO (Modification 2) found at the above URL's into Scala and includes a few simplifications (e.g., currently only works for linear kernels, dense data and binary classification).